[Eoas-seminar] GFDI Student Seminar - Bayesian Multimodel Analysis with Application to Microbial Soil Respiration Models

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Thu Apr 6 17:14:07 EDT 2017


*GFDI Student Seminar*

Hi GDF fellows. this coming Tuesday we have other seminar about Bayesian
analysis and its applications. Here is the information:

Presenter: * Ahmed Elshall*
Topic: *Theory and Application of Bayesian Multimodel Analysis with
Application to Microbial Soil Respiration Models*
Place:* Melvin Stern Seminar Room, #18 Keen Bldg*
Date: *Tuesday April 11th at 2:00 p.m.*
Abstract:

Models in biogeoscience are subject to parametric and conceptual
uncertainties. To accommodate different sources of uncertainty, multimodel
analysis such as model selection and model averaging are becoming popular.
This talk will present the theoretical and practical challenges of Bayesian
multimodel analysis, using a microbial soil respiration modeling example.
We are interested in these models because global soil respiration releases
about ten times more carbon dioxide to the atmosphere than all
anthropogenic emissions. Improving our understanding of microbial soil
respiration is essential for reducing the uncertainties of earth system
models. This study focuses on a poorly understood phenomena, which is the
soil microbial respiration pulses in response to episodic rainfall pulses,
the “Birch effect”. The hypothesis is that the “Birch effect” is generated
by three mechanisms that will be discussed during the talk. To test the
hypothesis, five microbial-enzyme models were developed and assessed
against field measurements from a semiarid Savannah that is characterized
by pulsed precipitation. These five models evolve stepwise such that the
first model includes none of the three mechanism, while the fifth model
includes the three mechanisms. The first part of the talk will illustrate
Bayesian model selection for the five models. Bayesian inference, which
involves updating a prior parameter disruption to a posterior parameter
distribution using a likelihood function, will be illustrated as well as
the estimation of Bayesian model evidence for model selection purpose. The
second part will discuss an important theoretical and practical challenge,
which is the effect of likelihood function selection on both Bayesian model
selection and model averaging. The talk will show that making valid
inference from scientific data is not a trivial task, since we are not only
uncertain about the candidate models, but also about the statistical
techniques that are used to appraise these models.

​Hope to see you all there! Refreshments will be provided.​


___________________________________
Roger B. Pacheco Castro
Geophysical Fluid Dynamics Institute
Florida State University
*Go Noles!*
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